update NS for FX tutorial for PyTorch v1.13

Summary:

Makes a couple of updates to ensure this tutorial still runs on 1.13:
1. changes the `qconfig_dict` argument of `prepare_fx` to `qconfig_mapping`
2. adds `example_inputs` to `prepare_fx`

Test plan:

Run the tutorial, it runs without errors on master

Author: vkuzo

prototype_source/fx_numeric_suite_tutorial.py

@@ -39,9 +39,10 @@
 import copy
 import torch
 import torchvision
-import torch.quantization
+import torch.ao.quantization
 import torch.ao.ns._numeric_suite_fx as ns
-import torch.quantization.quantize_fx as quantize_fx
+import torch.ao.quantization.quantize_fx as quantize_fx
+from torch.ao.quantization.qconfig_mapping import get_default_qconfig_mapping
 
 import matplotlib.pyplot as plt
 from tabulate import tabulate
@@ -68,25 +69,23 @@ def plot(xdata, ydata, xlabel, ylabel, title):
 mobilenetv2_float = torchvision.models.quantization.mobilenet_v2(
     pretrained=True, quantize=False).eval()
 
+# adjust the default qconfig to make the results more interesting to explore
+# 1. turn off quantization for the first couple of layers
+# 2. use MinMaxObserver for `features.17`, this should lead to worse
+# weight SQNR
+qconfig_mapping = get_default_qconfig_mapping('fbgemm')\
+    .set_module_name('features.0', None)\
+    .set_module_name('features.1', None)\
+    .set_module_name('features.0', torch.ao.quantization.default_qconfig)
+
 # create quantized model
-qconfig_dict = {
-    '': torch.quantization.get_default_qconfig('fbgemm'),
-    # adjust the qconfig to make the results more interesting to explore
-    'module_name': [
-        # turn off quantization for the first couple of layers
-        ('features.0', None),
-        ('features.1', None),
-        # use MinMaxObserver for `features.17`, this should lead to worse
-        # weight SQNR
-        ('features.17', torch.quantization.default_qconfig),
-    ]
-}
+
 # Note: quantization APIs are inplace, so we save a copy of the float model for
 # later comparison to the quantized model. This is done throughout the
 # tutorial.
-mobilenetv2_prepared = quantize_fx.prepare_fx(
-    copy.deepcopy(mobilenetv2_float), qconfig_dict)
 datum = torch.randn(1, 3, 224, 224)
+mobilenetv2_prepared = quantize_fx.prepare_fx(
+    copy.deepcopy(mobilenetv2_float), qconfig_mapping, (datum,))
 mobilenetv2_prepared(datum)
 # Note: there is a long standing issue that we cannot copy.deepcopy a
 # quantized model. Since quantization APIs are inplace and we need to use